In three-dimensional ultrasound imaging, or volume imaging, the acquisition of a three-dimensional image can be performed differently according to the probe. For instance, using an xMatrix probe, for example the X6-1 of Philips, a genuine 3D acquisition can be performed. Using a mechanical probe, for example the V6-2 of Philips, the 3D volume is obtained from the acquisition of multiple two-dimensional slices whose calibration is driven mechanically. Using a two-dimensional probe, a three-dimensional volume is made by conducting many two-dimensional scans that slice through the volume of interest. Hence, a multitude of two-dimensional images is acquired that lie next to another. Further, by proper image processing, a three-dimensional image of the volume of interest can be built out of the multitude of two-dimensional images. In all of the cases listed above, the three-dimensional information is displayed in proper form on a display for the user of the ultrasound system.
Further, so-called live three-dimensional imaging, or 4D imaging, is often used in clinical applications. In live three-dimensional imaging, a real-time view on the volume can be acquired enabling a user to view moving parts of the anatomical site, for example a beating heart or else. In the clinical application of live three-dimensional imaging there is sometimes a need to image a relatively small area of the heart such as a single valve, or a septal defect, and there is sometimes the need to image a large area of the heart such as an entire ventricle.
Two-dimensional image segmentation is a common task for radiologists. Image segmentation of three dimensional objects is often performed from multiple stacked two-dimensional segmentations. Image segmentation in three dimensions is less common. The extracted surface can be used either to quantify the volume of an organ or a tumor, or as a landmark to perform feature-based image registration. However, it is often tedious to manually segment an organ in a 3D image. While quantification and visualization tools are relatively available for 2D images, 3D volumes analysis is often done by hand through tedious procedures difficult to realize in clinical practice. Hence, such methods are quite inconvenient. Automatically conducted and precise segmentations are therefore needed, but difficult to obtain, especially in ultrasound images which are corrupted by a lot of noise and various artifacts.
Document US 2008/0139938 shows a system for acquiring, processing, and presenting boundaries of a cavity-tissue interface within a region-of-interest in an ultrasound image based upon the strength of signals of ultrasound echoes returning from structures within a region-of-interest (ROI). The segmentation of boundaries of cavity shapes occupying the region-of-interest utilizes cost function analysis of pixel sets occupying the cavity-tissue interface. The segmented shapes are further image processed to determine areas and volumes of the organ or structure containing the cavity within the region-of-interest.
Further, ultrasound is a largely used modality, especially during minimally invasive interventions, e.g. in the liver as it is harmless to the patient. Ultrasound images do not provide the same medical information compared to e.g. the computer tomography (CT) or magnetic resonance (MR) modality. All these modalities complement each other in providing comprehensive inside-body views. However, ultrasounds can have issue visualizing in between the ribs, as the ribs cast a shadow masking information. Also, ultrasound images have a limited field of view compared to computer tomography or magnetic resonance tomography. It has become a topic to align computer tomography or magnetic resonance tomography data of an object within a human body with ultrasound image data. CT or MR are usually acquired prior to the use of ultrasound and contain precise information about e.g. a tumor shape and location. During the use of ultrasound imaging, it is desired to keep at all times the annotated data, e.g. tumor shape and location acquired via CT and/or MR, aligned with the ultrasound data.
Further, even if no further modalities are used, ultrasound images might be acquired from different viewpoints. Hence, it is a further topic to register multiple ultrasound images towards each other.
There is a need for improved automatic or at least computer-aided segmentation and registration tools.